Breast density is a significant breast cancer risk factor measured from mammograms. To date, most work in breast density has been performed with raw data using an operator assisted labeling method. Although breast density is a significant breast cancer risk factor, it is not currently used for risk assessments in a clinical setting, partly due to lack of standardization and automation. Evidence suggests that the spatial variation in mammograms may also be associated with risk. The variation in calibrated mammograms as a breast cancer risk factor was investigated and its relationship with other measures of breast density was explored using full field digital mammography (FFDM) as described herein. For additional discussion of the variation measure, see Heine, J. J. et al. “Calibrated measures for breast density estimation,” Acad Radiol, vol. 18, pp. 547-55, May 2011; Heine, J. J. et al., “A Quantitative Description of the Percentage of Breast Density Measurement Using Full-field Digital Mammography,” Acad Radiol, vol. 18, pp. 556-64, May 2011.
There are various methods used to assess breast density. For the most part, breast density and breast cancer associations have been developed with measurements that did not consider the inter-image acquisition technique differences. In particular, the operator-assisted percentage of breast density approach (or PD) has shown repeatedly to correlate well with breast cancer without considering the acquisition technique. Methods for automating PD are not widely used. An alternative method of assessing breast density is to calibrate, or adjust, for the acquisition technique differences.
Calibration should reduce unwanted measurement variation and produce a measure of mammographic density that shows stronger associations with breast cancer than non-calibrated methods such as PD. However, measurements based on calibration with digitized film mammography have produced mixed findings. Some work shows that calibration does not produce anything beyond PD. Other work shows that calibration strengthens the breast density associations with film mammography. For example, using FFDM, studies have shown that calibration can be used to both describe PD and to develop new measures of breast density. One new measure is calculated as the standard deviation (SD) of the calibrated pixels within the breast area, which captures spatial variation. This measure provided stronger associations with breast cancer than PD in some studies.
The calibration produces image data normalized for the inter-image acquisition technique differences at the pixel level (or more coarse scales) referred to as the percent glandular representation, which is a normalized effective x-ray attenuation coefficient metric. Differences in the compressed breast thickness, target/filter combination, x-ray tube voltage and exposure are rectified by the calibration process. There are many technical problems that if not addressed will introduce considerable error into the calibration output.
In one study, a matched case-control analysis was used to assess a spatial variation breast density measure in calibrated FFDM images, normalized for the image acquisition technique variation. Three measures of breast density were compared between cases and controls: (a) the calibrated average measure, (b) the calibrated variation measure, and (c) the standard percentage of breast density (PD) measure derived from operator-assisted labeling. Linear correlation and statistical relationships between these three breast density measures were also investigated.
Risk estimates associated with the lowest to highest quartiles for the calibrated variation measure were greater in magnitude [odds ratios: 1.0 (ref.), 3.5, 6.3, and 11.3] than the corresponding risk estimates for quartiles of the standard PD measure [odds ratios: 1.0 (ref.), 2.3, 5.6, and 6.5] and the calibrated average measure [odds ratios: 1.0 (ref.), 2.4, 2.3, and 4.4]. The three breast density measures were highly correlated, showed an inverse relationship with breast area, and related by a mixed distribution relationship.
The three measures of breast density capture different attributes of the same data field. These findings indicate the variation measure is a viable automated method for assessing breast density. Insights gained by this work may be used to develop a standard for measuring breast density.